Optimizing kernel size in generalized auto-calibrating partially parallel acquisition in parallel magnetic resonance imaging (original) (raw)
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A NEW METHOD FOR DATA ACQUISITION AND IMAGE RECONSTRUCTION IN PARALLEL MAGNETIC RESONANCE IMAGING
We propose a novel data acquisition and image reconstruction method for parallel magnetic resonance imaging (MRI). The proposed method improves the GRAPPA (Generalized Auto-calibrating Partially Parallel Acquisitions) method by simultaneously collecting data using the body coil in addition to localized surface coils. The body coil data is included in the GRAPPA reconstruction as an additional coil. The reconstructed body coil image shows greater uniformity over the field of view than the conventional sum-of-squares reconstruction that is conventionally used with GRAPPA. The body coil image can also be used to correct for spatial inhomogeneity in the sum-of-squares image. The proposed method is tested using numerical and real MRI phantom data.
New approach for data acquisition and image reconstruction in parallel magnetic resonance imaging
2009
In this study, we propose a novel data acquisition and image reconstruction method for parallel magnetic resonance imaging (MRI). The proposed method improves the GRAPPA algorithm by simultaneously collecting data using the body coil in addition to localized surface coils. The body coil data is included in the GRAPPA reconstruction as an additional coil. The reconstructed body coil image shows greater uniformity over the field of view than the conventional sum-of-squares (SoS) reconstruction that is conventionally used with GRAPPA. The body coil image can also be used to correct for spatial inhomogeneity in the SoS image. The algorithm has been tested using numerical and real MRI phantom data.
Self-calibrating parallel imaging with automatic coil sensitivity extraction
Magnetic Resonance in Medicine, 2002
Calibration of the spatial sensitivity functions of coil arrays is a crucial element in parallel magnetic resonance imaging (PMRI). The most common approach has been to measure coil sensitivities directly using one or more low-resolution images acquired before or after accelerated data acquisition. However, since it is difficult to ensure that the patient and coil array will be in exactly the same positions during both calibration scans and accelerated imaging, this approach can introduce sensitivity miscalibration errors into PMRI reconstructions. This work shows that it is possible to extract sensitivity calibration images directly from a fully sampled central region of a variable-density k-space acquisition. These images have all the features of traditional PMRI sensitivity calibrations and therefore may be used for any PMRI reconstruction technique without modification. Because these calibration data are acquired simultaneously with the data to be reconstructed, errors due to sensitivity miscalibration are eliminated. In vivo implementations of self-calibrating parallel imaging using a flexible coil array are demonstrated in abdominal imaging and in real-time cardiac imaging studies. Magn Reson Med 47:529 -538, 2002.
Regularization of parallel MRI reconstruction using in vivo coil sensitivities
2009
Parallel MRI can achieve increased spatiotemporal resolution in MRI by simultaneously sampling reduced k-space data with multiple receiver coils. One requirement that different parallel MRI techniques have in common is the need to determine spatial sensitivity information for the coil array. This is often done by smoothing the raw sensitivities obtained from low-resolution calibration images, for example via polynomial fitting. However, this sensitivity post-processing can be both time-consuming and error-prone. Another important factor in Parallel MRI is noise amplification in the reconstruction, which is due to non-unity transformations in the image reconstruction associated with spatially correlated coil sensitivity profiles. Generally, regularization approaches, such as Tikhonov and SVD-based methods, are applied to reduce SNR loss, at the price of introducing residual aliasing. In this work, we present a regularization approach using in vivo coil sensitivities in parallel MRI to overcome these potential errors into the reconstruction. The mathematical background of the proposed method is explained, and the technique is demonstrated with phantom images. The effectiveness of the proposed method is then illustrated clinically in a whole-heart 3D cardiac MR acquisition within a single breath-hold. The proposed method can not only overcome the sensitivity calibration problem, but also suppress a substantial portion of reconstruction-related noise without noticeable introduction of residual aliasing artifacts.
Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction
IEEE Transactions on Medical Imaging, 2000
The amount of calibration data needed to produce images of adequate quality can prevent autocalibrating parallel imaging reconstruction methods like Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) from achieving a high total acceleration factor. To improve the quality of calibration when the number of auto-calibration signal (ACS) lines is restricted, we propose a sparsity-promoting regularized calibration method that finds a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images. Several experiments evaluate the performance of the proposed method relative to un-regularized and existing regularized calibration methods for both low-quality and underdetermined fits from the ACS lines. These experiments demonstrate that the proposed method, like other regularization methods, is capable of mitigating noise amplification, and in addition, the proposed method is particularly effective at minimizing coherent aliasing artifacts caused by poor kernel calibration in real data. Using the proposed method, we can increase the total achievable acceleration while reducing degradation of the reconstructed image better than existing regularized calibration methods. Parallel imaging with multi-channel receive array coils and auto-calibrating reconstruction methods like Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) [1]
Automatic High-Bandwidth Calibration and Reconstruction of Arbitrarily Sampled Parallel MRI
PLoS ONE, 2014
Today, many MRI reconstruction techniques exist for undersampled MRI data. Regularization-based techniques inspired by compressed sensing allow for the reconstruction of undersampled data that would lead to an ill-posed reconstruction problem. Parallel imaging enables the reconstruction of MRI images from undersampled multi-coil data that leads to a wellposed reconstruction problem. Autocalibrating pMRI techniques encompass pMRI techniques where no explicit knowledge of the coil sensivities is required. A first purpose of this paper is to derive a novel autocalibration approach for pMRI that allows for the estimation and use of smooth, but high-bandwidth coil profiles instead of a compactly supported kernel. These high-bandwidth models adhere more accurately to the physics of an antenna system. The second purpose of this paper is to demonstrate the feasibility of a parameter-free reconstruction algorithm that combines autocalibrating pMRI and compressed sensing. Therefore, we present several techniques for automatic parameter estimation in MRI reconstruction. Experiments show that a higher reconstruction accuracy can be had using high-bandwidth coil models and that the automatic parameter choices yield an acceptable result.
Magma: Magnetic Resonance Materials in Physics, Biology, and Medicine, 2001
Parallel magnetic resonance imaging (MRI) techniques use spatial information from arrays of radiofrequency (RF) detector coils to accelerate imaging. A number of parallel MRI techniques have been described in recent years, and numerous clinical applications are currently being explored. The advent of practical parallel imaging presents various challenges for image reconstruction and RF system design. Recent advances in tailored SiMultaneous Acquisition of Spatial Harmonics (SMASH) image reconstructions are summarized. These advances enable robust SMASH imaging in arbitrary image planes with a wide range of coil array geometries. A generalized formalism is described which may be used to understand the relations between SMASH and SENSE, to derive typical implementations of each as special cases, and to form hybrid techniques combining some of the advantages of both. Accurate knowledge of coil sensitivities is crucial for parallel MRI, and errors in calibration represent one of the most common and the most pernicious sources of error in parallel image reconstructions. As one example, motion of the patient and/or the coil array between the sensitivity reference scan and the accelerated acquisition can lead to calibration errors and reconstruction artifacts. Self-calibrating parallel MRI approaches that address this problem by eliminating the need for external sensitivity references are reviewed. The ultimate achievable signal-to-noise ratio (SNR) for parallel MRI studies is closely tied to the geometry and sensitivity patterns of the coil arrays used for spatial encoding. Several parallel imaging array designs that depart from the traditional model of overlapped adjacent loop elements are described.
Autocalibrated coil sensitivity estimation for parallel imaging
NMR in Biomedicine, 2006
Parallel imaging has proven to be a robust solution to the problem of acquisition speed in MRI. These methods are based on extracting spatial information from an array of multiple surface coils in order to speed up image acquisition. One of the most essential elements of any parallel imaging method is the information describing the coil sensitivity distribution throughout the sample. This paper covers some of the advanced methods to obtain coil sensitivity-related information, focusing particularly on the class of methods referred to as autocalibrating. These methods all acquire the data for coil sensitivity estimation directly before, during or directly after the reduced data acquisition. After a review of standard methods for coil sensitivity estimation, some of the basic and advanced autocalibrating methods are reviewed, and some example applications shown.
CALIBRATION-LESS MULTI-COIL MR IMAGE RECONSTRUCTION
State-of-the-art parallel MRI techniques either explicitly or implicitly require certain parameters to be estimated, e.g. the sensitivity map for SENSE, SMASH and interpolation weights for GRAPPA, SPIRiT. Thus all these techniques are sensitive to the calibration (parameter estimation) stage. In this work, we have proposed a parallel MRI technique that does not require any calibration but yields reconstruction results that are at par with (or even better than) state-of-the-art methods in parallel MRI. Our proposed method required solving non-convex analysis and synthesis prior joint-sparsity problems. This work also derives the algorithms for solving them. Experimental validation was carried out on two real datasets (8 channel brain and 4 channel UBC Phantom) and one simulated (8 channel Shepp-Logan phantom) dataset. Two sampling methods were used – Variable Density Random sampling and non-Cartesian Radial sampling. For the brain data, acceleration factor of 4 was used and for the others acceleration factor of 6 was used. The reconstruction results were quantitatively evaluated based on the Normalised Mean Squared Error between the reconstructed image and the originals. The qualitative evaluation was based on the actual reconstructed images. Results show that the previous methods (CS SENSE, GRAPPA/GROG and L1SPIRiT) are sensitive to the calibration stage and the reconstruction accuracy varies between 15 to 30%. Our proposed method yields reconstruction results that are at par with (or even better than) the best results obtained from state-of-the-art techniques.
IIR GRAPPA for parallel MR image reconstruction
Magnetic Resonance in Medicine, 2010
Accelerated parallel MRI has advantage in imaging speed, and its image quality has been improved continuously in recent years. This paper introduces a two-dimensional infinite impulse response model of inverse filter to replace the finite impulse response model currently used in generalized autocalibrating partially parallel acquisitions class image reconstruction methods. The infinite impulse response model better characterizes the correlation of k-space data points and better approximates the perfect inversion of parallel imaging process, resulting in a novel generalized image reconstruction method for accelerated parallel MRI. This k-space-based reconstruction method includes the conventional generalized autocalibrating partially parallel acquisitions class methods as special cases and has a new infinite impulse response data estimation mechanism for effective improvement of image quality.